Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction (2020.acl-main)
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| Challenge: | Existing methods to extract emotions and causes from unannotated text are pipelined, causing error propagation. |
| Approach: | They propose to transform a task into a procedure of parsing-like directed graph construction . they propose to generate a directed graph with labeled edges based on a sequence of actions . |
| Outcome: | The proposed method outperforms the state-of-the-art methods by 6.71% (p0.01) in F1 measure. |
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Effective Inter-Clause Modeling for End-to-End Emotion-Cause Pair Extraction (2020.acl-main)
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| Challenge: | Emotion-cause pair extraction aims to extract all emotion clauses coupled with their cause clauses from a given document. |
| Approach: | They propose a one-step neural approach which emphasizes inter-clause modeling to perform end-to-end extraction. |
| Outcome: | The proposed method outperforms existing methods in the extraction of emotion-cause pairs . it emphasizes inter-clause modeling to perform end-to-end extraction . |
Emotion-Cause Pair Extraction as Sequence Labeling Based on A Novel Tagging Scheme (2020.emnlp-main)
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| Challenge: | Existing methods to extract emotions and causes from unannotated emotion texts are labor intensive and limited applications in real-world scenarios. |
| Approach: | They propose a novel task to find emotions and corresponding causes in unannotated emotion texts. |
| Outcome: | The proposed model outperforms the state-of-the-art method by 2.26% (p0.001) in F1 measure. |
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts (P19-1)
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| Challenge: | Emotion cause extraction (ECE) aims at extracting potential causes behind certain emotions in text. |
| Approach: | They propose a 2-step task to extract potential pairs of emotions and corresponding causes in a document. |
| Outcome: | The proposed task is based on a benchmark emotion cause corpus. |
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network (2020.coling-main)
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| Challenge: | Emotion-cause pair extraction (ECPE) aims to extract emotion expressions and their corresponding causes in a document simultaneously. |
| Approach: | They propose to model pair-level contexts so that to capture dependency information among local neighborhood candidate pairs. |
| Outcome: | The proposed model extracts emotion-cause pairs and their causes from documents . it is based on a benchmark Chinese emotion-case pair extraction corpus . |
Pair-Based Joint Encoding with Relational Graph Convolutional Networks for Emotion-Cause Pair Extraction (2022.emnlp-main)
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| Challenge: | Emotion cause pair extraction (ECPE) aims to extract emotion clauses and corresponding cause clauses. |
| Approach: | They propose a novel task called emotion-cause pair extraction to extract emotion clauses and corresponding cause clauses. |
| Outcome: | The proposed task can extract emotion clauses and cause clauses, and achieve state-of-the-art performance on the Chinese benchmark corpus. |
ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction (2020.acl-main)
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| Challenge: | a new task, called emotion-cause pair extraction, has emerged in text emotion analysis . a 2D representation scheme is proposed to represent the emotion-case pairs . |
| Approach: | They propose a 2D approach to represent emotion-cause pairs by a 3D representation scheme. |
| Outcome: | The proposed approach improves the state-of-the-art on the emotion cause corpus . the proposed approach is based on a two-step framework with flaws . |
A Unified Sequence Labeling Model for Emotion Cause Pair Extraction (2020.coling-main)
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| Challenge: | Existing methods for emotion-cause pair extraction cannot distinguish emotion-caused pairs from each other . Existing approaches may suffer from possible cascading errors . |
| Approach: | They propose to assign emotion type labels to emotion and cause clauses so that they can be easily distinguished. |
| Outcome: | The proposed method can extract multiple emotion-cause pairs in an end-to-end fashion. |
End-to-End Emotion-Cause Pair Extraction based on Sliding Window Multi-Label Learning (2020.emnlp-main)
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| Challenge: | Existing methods to extract potential pairs of emotions ignore the fact that the cause and the emotion it triggers are inseparable. |
| Approach: | They propose two frameworks that combine multi-label learning and multi-labeled learning to extract emotion clauses . they evaluate a benchmark emotion cause corpus and find the best performance . |
| Outcome: | The proposed frameworks achieve the best performance among all compared systems on the ECPE task. |
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction (2024.findings-emnlp)
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Yuncheng Hua, Yujin Huang, Shuo Huang, Tao Feng, Lizhen Qu, Christopher Bain, Richard Bassed, Reza Haf
| Challenge: | Emotion-cause pair extraction is a task that aims to extract emotions and the events causing such emotions. |
| Approach: | They propose a deep latent model which captures the underlying latent structures of data and utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains. |
| Outcome: | The proposed model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on an English benchmark in terms of weighted-average F1 score. |
Enhancing Emotion-Cause Pair Extraction in Conversations via Center Event Detection and Reasoning (2024.findings-emnlp)
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| Challenge: | Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify emotion utterances and their corresponding cause utterrances in unannotated conversations. |
| Approach: | They propose a new method to identify emotion utterances and their corresponding cause utterrances in unannotated conversations by using a center event-aware graph. |
| Outcome: | The proposed model outperforms existing methods and achieves state-of-the-art performance across three benchmark datasets. |